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Record W6976683628 · doi:10.60692/qjdre-tga04

Iodine oxide in the global marine boundary layer

2015· article· en· W6976683628 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsMakivik Corporation
Fundersnot available
KeywordsIodineHalogenOxidizing agentAtmospheric chemistryBoundary layerOzoneIodateAtmosphere (unit)

Abstract

fetched live from OpenAlex

Abstract. Emitted mainly by the oceans, iodine is a halogen compound important for atmospheric chemistry due to its high ozone depletion potential and effect on the oxidizing capacity of the atmosphere. Here we present a comprehensive data set of iodine oxide (IO) measurements in the open marine boundary layer (MBL) made during the Malaspina 2010 circumnavigation. Results show IO mixing ratios ranging from 0.4 to 1 pmol mol−1 (30% uncertainty) and, complemented with additional field campaigns, this data set confirms through observations the ubiquitous presence of reactive iodine chemistry in the global marine environment. We use a global model with organic (CH3I, CH2ICl, CH2I2 and CH2IBr) and inorganic (HOI and I2) iodine ocean emissions to investigate the contribution of the different iodine source gases to the budget of IO in the global MBL. In agreement with previous estimates, our results indicate that, globally averaged, the abiotic precursors contribute about 75 % to the IO budget. However, this work reveals a strong geographical pattern in the contribution of organic vs. inorganic precursors to reactive iodine in the global MBL.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.177

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.204
Teacher spread0.167 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

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